Yunfei Teng, Anna Choromanska, et al.
ECML PKDD 2022
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly. We present the Generative Toolkit for Scientific Discovery (GT4SD). This extensible open-source library enables scientists, developers, and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.
Yunfei Teng, Anna Choromanska, et al.
ECML PKDD 2022
Alain Vaucher, Philippe Schwaller, et al.
AMLD EPFL 2022
Advait Parulekar, Karthikeyan Shanmugam, et al.
ICML 2023
Jannis Born, Matteo Manica, et al.
iScience